Vector meson production in pp collisions at s=7 TeV\sqrt{s}=7\rm{~TeV}, measured with the ALICE detector

Vector mesons are key probes of the hot and dense state of strongly interacting matter produced in heavy ion collisions. Their dileptonic decay channel is particularly suitable for these studies, since dileptons have negligible final state interactions in hadronic matter. A preliminary measurement of the $\phi$ and $\omega$ differential cross sections was performed by the ALICE experiment in pp collisions at $\sqrt{s}=7$ TeV, through their decay in muon pairs. The $p_{\rm T}$ and rapidity regions covered in this analysis are $p_{\rm T}>1$ GeV$/c$ and $2.5 < y < 4$.Comment: 4 pages, 4, figures, proceedings of the Quark Matter 2011 conferenc

Editorial: new challenges In theory and practice of corporate governance

The aim of international conference “New Challenges in Corporate Governance: Theory And Practice” is to move the field closer to a global theory by advancing our understanding of corporate governance, which combines insights from the literature on firm governance bundles with insights from the national governance systems literature, investigating new perspectives and challenges for corporate governance and outlining possible scenarios of its development

Social innovation practices: focus on success factors for crowdfunding

This article explores what are the success factor for interaction with platforms of crowdfunding in Italy. Through a Principal Component Analysis we outline three variables and through a multiple regression analysis we demonstrate that the interaction on crowdfunding is positive correlated with socio-economic propensity and cultural level. The analysis has been conducted on a sample of 316 of projects funded in the Crowdfunding platform Produzioni dal Basso, the first platform born in Italy. We draw on SD logic and relationship marketing to underscore the importance of networks of actors and integration to create a co-creation of value. This view emphasizes the social and economic factors that influence, and are influenced by, crowdfundin

Enterprise Risk Management, Corporate Governance And Systemic Risk: Some Research Perspectives

The general goal of Enterprise Risk Management (ERM) processes is to generate economic value through the coverage of firm business risk, on the one hand, and by exploiting the positive side of uncertainty conditions, on the other hand. The increasing attention attributed to ERM in the creation of economic value has led to even greater interactions between risk management mechanisms and the corporate governance system. In other words, in the last two decades, the relationships between corporate governance and ERM increased since the ERM processes have been considered more and more as critical drivers to combine strategic objectives with relative low volatility of company performance. The basic idea is that a good corporate governance system must deal about specific risks along with their interactions and, at the same time, the firm’s business risk as a whole. Moreover, an efficient and effective ERM system provides clear information about linkages between strategic opportunities and risk exposure and offers tools able to manage in an optimal way the negative side of business risk (or downside risk) as wellas its positive side (or upside risk). Accordingly, extant studies concerning the relationships between ERM and corporate governance have been focusing on a micro-level of analyses (i.e., the individual organization) and, specifically, on a firm’s benefits that stem from the adoption of proper ERM processes that are consistent with corporate governance goals and are able to sustain the increase of economic value while maintaining a bearable business risk over time. From our initial analyses, a gap in literature arises. We argue that the interdependence between ERM and corporate governance may be analyzed from a broader point of view as well (i.e., the firm and its task environment composed by its suppliers, customers, and partners). In particular, our research idea is to enlarge traditional studies about interrelations between corporate governance and ERM taking into account whether such interrelations could be a driver of risk transfer from the focal organization to other organizations that belong to its task environment. Moreover, this study aims to deepen the mechanisms by which the transfer of risk from a focal organization to its task environment may foster the emergence of systemic risk, i.e., a macro risk coming from domino and/or network effects. Therefore, our paper aims to find new research areas by combining micro and macro issues tied to corporate governance, ERM and systemic risk. The starting point of our work is the three following assumptions: 1) The compliance of a firm to ERM processes as well as to corporate governance rules implies the reduction as much as possible of firm business risk; 2) The reduction of the firm business risk leads to externalizing the firm business risk through risk-sharing mechanisms; 3) The risk-sharing may arise like a driver of systemic risk especially in those industries featured by strong network interrelations. Starting from the above assumptions, the paper goal is to open a new research area which combines four academic fields (ERM, corporate governance, corporate finance, and macro-finance). So far, our initial findings tell us that the following research questions arise: RQ1: What are the conditions under which the transfer of business risk towards organizations that belong to a firm task environment is likely to become a source of systemic risk in a specific industry? RQ2: How does the capital structure of a focal firm affect its propensity to transfer business risk not only to commercial but also to financial stakeholders included in firm task environment? RQ3: How does the transfer of business risk influence the capital cost of the focal firm as well as of the organizations that absorbed such risk

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases

pRb2/p130: a new candidate for retinoblastoma tumor formation

n/

How Noisy Data Affects Geometric Semantic Genetic Programming

Noise is a consequence of acquiring and pre-processing data from the environment, and shows fluctuations from different sources---e.g., from sensors, signal processing technology or even human error. As a machine learning technique, Genetic Programming (GP) is not immune to this problem, which the field has frequently addressed. Recently, Geometric Semantic Genetic Programming (GSGP), a semantic-aware branch of GP, has shown robustness and high generalization capability. Researchers believe these characteristics may be associated with a lower sensibility to noisy data. However, there is no systematic study on this matter. This paper performs a deep analysis of the GSGP performance over the presence of noise. Using 15 synthetic datasets where noise can be controlled, we added different ratios of noise to the data and compared the results obtained with those of a canonical GP. The results show that, as we increase the percentage of noisy instances, the generalization performance degradation is more pronounced in GSGP than GP. However, in general, GSGP is more robust to noise than GP in the presence of up to 10% of noise, and presents no statistical difference for values higher than that in the test bed.Comment: 8 pages, In proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, German

Bamboo trusses with low cost and high ductility joints

Innovative solutions of joints for bamboo trusses are presented. Experimental tests show the performances and the high level of ductility of the proposed technique, joined with simplicity in the concept of the joints, low level of technology and low cost of all used materials. It can permit a proper dissemination and a sustainable maintenance in developing countries